Analysis of GLDS-46 from NASA GeneLab

This R markdown file was auto-generated by the iDEP website Using iDEP 0.91, originally by Steven

Ge SX, Son EW, Yao R: iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 2018, 19(1):534. PMID:30567491

1. Read data

First we set up the working directory to where the files are saved.

 setwd('~/Documents/HTML_R/GLDS46')

R packages and iDEP core Functions. Users can also download the iDEP_core_functions.R file. Many R packages needs to be installed first. This may take hours. Each of these packages took years to develop.So be a patient thief. Sometimes dependencies needs to be installed manually. If you are using an older version of R, and having trouble with package installation, try un-install the current version of R, delete all folders and files (C:/Program Files/R/R-3.4.3), and reinstall from scratch.

 if(file.exists('iDEP_core_functions.R'))
    source('iDEP_core_functions.R') else 
    source('https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/iDEP_core_functions.R')

We are using the downloaded gene expression file where gene IDs has been converted to Ensembl gene IDs. This is because the ID conversion database is too large to download. You can use your original file if your file uses Ensembl ID, or you do not want to use the pathway files available in iDEP (or it is not available).

 inputFile <- 'GLDS46_Expression.csv'
 sampleInfoFile <- 'GLDS46_Sampleinfo.csv'
 gldsMetadataFile <- 'GLDS46_Metadata.csv'
 geneInfoFile <- 'Arabidopsis_thaliana__athaliana_eg_gene_GeneInfo.csv' #Gene symbols, location etc. 
 geneSetFile <- 'Arabidopsis_thaliana__athaliana_eg_gene.db'  # pathway database in SQL; can be GMT format 
 STRING10_speciesFile <- 'https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/STRING10_species.csv'

Parameters for reading data

 input_missingValue <- 'geneMedian' #Missing values imputation method
 input_dataFileFormat <- 1  #1- read counts, 2 FKPM/RPKM or DNA microarray
 input_minCounts <- 0.5 #Min counts
 input_NminSamples <- 1 #Minimum number of samples 
 input_countsLogStart <- 4  #Pseudo count for log CPM
 input_CountsTransform <- 1 #Methods for data transformation of counts. 1-EdgeR's logCPM 2-VST, 3-rlog 
readMetadata.out <- readMetadata(gldsMetadataFile)
library(knitr)   #  install if needed. for showing tables with kable
library(kableExtra)
kable( readMetadata.out ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
ATM1_HZE_15hpostIR_5do_Rep1 ATM1_HZE_15hpostIR_5do_Rep2 ATM1_HZE_12hpostIR_5do_Rep1 ATM1_HZE_12hpostIR_5do_Rep2 ATM1_HZE_24hpostIR_6do_Rep1 ATM1_HZE_24hpostIR_6do_Rep2 ATM1_HZE_6hpostIR_5do_Rep1 ATM1_HZE_6hpostIR_5do_Rep2 ATM1_noIR_CTRL_5do_Rep1 ATM1_noIR_CTRL_5do_Rep2 ATM1_noIR_CTRL_6do_Rep1 ATM1_noIR_CTRL_6do_Rep2 ATM1_YR_15hpostIR_5do_Rep1 ATM1_YR_15hpostIR_5do_Rep2 ATM1_YR_12hpostIR_5do_Rep1 ATM1_YR_12hpostIR_5do_Rep2 ATM1_YR_24hpostIR_6do_Rep1 ATM1_YR_24hpostIR_6do_Rep2 ATM1_YR_6hpostIR_5do_Rep1 ATM1_YR_6hpostIR_5do_Rep2 WT_HZE_15hpostIR_5do_Rep1 WT_HZE_15hpostIR_5do_Rep2 WT_HZE_12hpostIR_5do_Rep1 WT_HZE_12hpostIR_5do_Rep2 WT_HZE_24hpostIR_6do_Rep1 WT_HZE_24hpostIR_6do_Rep2 WT_HZE_3hpostIR_5do_Rep1 WT_HZE_3hpostIR_5do_Rep2 WT_HZE_6hpostIR_5do_Rep1 WT_HZE_6hpostIR_5do_Rep2 WT_noIR_CTRL_5do_Rep1 WT_noIR_CTRL_5do_Rep2 WT_noIR_CTRL_6do_Rep1 WT_noIR_CTRL_6do_Rep2 WT_YR_15hpostIR_5do_Rep1 WT_YR_15hpostIR_5do_Rep2 WT_YR_12hpostIR_5do_Rep1 WT_YR_12hpostIR_5do_Rep2 WT_YR_24hpostIR_6do_Rep1 WT_YR_24hpostIR_6do_Rep2 WT_YR_3hpostIR_5do_Rep1 WT_YR_3hpostIR_5do_Rep2 WT_YR_6hpostIR_5do_Rep1 WT_YR_6hpostIR_5do_Rep2
Sample.LongId Atha.Ws.sl.ATM1.HZE.1.5h.postIR.5do.Rep1.Array Atha.Ws.sl.ATM1.HZE.1.5h.postIR.5do.Rep2.Array Atha.Ws.sl.ATM1.HZE.12h.postIR.5do.Rep1.Array Atha.Ws.sl.ATM1.HZE.12h.postIR.5do.Rep2.Array Atha.Ws.sl.ATM1.HZE.24h.postIR.6do.Rep1.Array Atha.Ws.sl.ATM1.HZE.24h.postIR.6do.Rep2.Array Atha.Ws.sl.ATM1.HZE.6h.postIR.5do.Rep1.Array Atha.Ws.sl.ATM1.HZE.6h.postIR.5do.Rep2.Array Atha.Ws.sl.ATM1.noIR.CTRL.5do.Rep1.Array Atha.Ws.sl.ATM1.noIR.CTRL.5do.Rep2.Array Atha.Ws.sl.ATM1.noIR.CTRL.6do.Rep1.Array Atha.Ws.sl.ATM1.noIR.CTRL.6do.Rep2.Array Atha.Ws.sl.ATM1.YR.1.5h.postIR.5do.Rep1.Array Atha.Ws.sl.ATM1.YR.1.5h.postIR.5do.Rep2.Array Atha.Ws.sl.ATM1.YR.12h.postIR.5do.Rep1.Array Atha.Ws.sl.ATM1.YR.12h.postIR.5do.Rep2.Array Atha.Ws.sl.ATM1.YR.24h.postIR.6do.Rep1.Array Atha.Ws.sl.ATM1.YR.24h.postIR.6do.Rep2.Array Atha.Ws.sl.ATM1.YR.6h.postIR.5do.Rep1.Array Atha.Ws.sl.ATM1.YR.6h.postIR.5do.Rep2.Array Atha.Ws.sl.WT.HZE.1.5h.postIR.5do.Rep1.Array Atha.Ws.sl.WT.HZE.1.5h.postIR.5do.Rep2.Array Atha.Ws.sl.WT.HZE.12h.postIR.5do.Rep1.Array Atha.Ws.sl.WT.HZE.12h.postIR.5do.Rep2.Array Atha.Ws.sl.WT.HZE.24h.postIR.6do.Rep1.Array Atha.Ws.sl.WT.HZE.24h.postIR.6do.Rep2.Array Atha.Ws.sl.WT.HZE.3h.postIR.5do.Rep1.Array Atha.Ws.sl.WT.HZE.3h.postIR.5do.Rep2.Array Atha.Ws.sl.WT.HZE.6h.postIR.5do.Rep1.Array Atha.Ws.sl.WT.HZE.6h.postIR.5do.Rep2.Array Atha.Ws.sl.WT.noIR.CTRL.5do.Rep1.Array Atha.Ws.sl.WT.noIR.CTRL.5do.Rep2.Array Atha.Ws.sl.WT.noIR.CTRL.6do.Rep1.Array Atha.Ws.sl.WT.noIR.CTRL.6do.Rep2.Array Atha.Ws.sl.WT.YR.1.5h.postIR.5do.Rep1.Array Atha.Ws.sl.WT.YR.1.5h.postIR.5do.Rep2.Array Atha.Ws.sl.WT.YR.12h.postIR.5do.Rep1.Array Atha.Ws.sl.WT.YR.12h.postIR.5do.Rep2.Array Atha.Ws.sl.WT.YR.24h.postIR.6do.Rep1.Array Atha.Ws.sl.WT.YR.24h.postIR.6do.Rep2.Array Atha.Ws.sl.WT.YR.3h.postIR.5do.Rep1.Array Atha.Ws.sl.WT.YR.3h.postIR.5do.Rep2.Array Atha.Ws.sl.WT.YR.6h.postIR.5do.Rep1.Array Atha.Ws.sl.WT.YR.6h.postIR.5do.Rep2.Array
Sample.Id Atha.Ws.sl.ATM1.HZE.1.5h.postIR.5do_Rep1 Atha.Ws.sl.ATM1.HZE.1.5h.postIR.5do_Rep2 Atha.Ws.sl.ATM1.HZE.12h.postIR.5do_Rep1 Atha.Ws.sl.ATM1.HZE.12h.postIR.5do_Rep2 Atha.Ws.sl.ATM1.HZE.24h.postIR.6do_Rep1 Atha.Ws.sl.ATM1.HZE.24h.postIR.6do_Rep2 Atha.Ws.sl.ATM1.HZE.6h.postIR.5do_Rep1 Atha.Ws.sl.ATM1.HZE.6h.postIR.5do_Rep2 Atha.Ws.sl.ATM1.noIR.CTRL.5do_Rep1 Atha.Ws.sl.ATM1.noIR.CTRL.5do_Rep2 Atha.Ws.sl.ATM1.noIR.CTRL.6do_Rep1 Atha.Ws.sl.ATM1.noIR.CTRL.6do_Rep2 Atha.Ws.sl.ATM1.YR.1.5h.postIR.5do_Rep1 Atha.Ws.sl.ATM1.YR.1.5h.postIR.5do_Rep2 Atha.Ws.sl.ATM1.YR.12h.postIR.5do_Rep1 Atha.Ws.sl.ATM1.YR.12h.postIR.5do_Rep2 Atha.Ws.sl.ATM1.YR.24h.postIR.6do_Rep1 Atha.Ws.sl.ATM1.YR.24h.postIR.6do_Rep2 Atha.Ws.sl.ATM1.YR.6h.postIR.5do_Rep1 Atha.Ws.sl.ATM1.YR.6h.postIR.5do_Rep2 Atha.Ws.sl.WT.HZE.1.5h.postIR.5do_Rep1 Atha.Ws.sl.WT.HZE.1.5h.postIR.5do_Rep2 Atha.Ws.sl.WT.HZE.12h.postIR.5do_Rep1 Atha.Ws.sl.WT.HZE.12h.postIR.5do_Rep2 Atha.Ws.sl.WT.HZE.24h.postIR.6do_Rep1 Atha.Ws.sl.WT.HZE.24h.postIR.6do_Rep2 Atha.Ws.sl.WT.HZE.3h.postIR.5do_Rep1 Atha.Ws.sl.WT.HZE.3h.postIR.5do_Rep2 Atha.Ws.sl.WT.HZE.6h.postIR.5do_Rep1 Atha.Ws.sl.WT.HZE.6h.postIR.5do_Rep2 Atha.Ws.sl.WT.noIR.CTRL.5do_Rep1 Atha.Ws.sl.WT.noIR.CTRL.5do_Rep2 Atha.Ws.sl.WT.noIR.CTRL.6do_Rep1 Atha.Ws.sl.WT.noIR.CTRL.6do_Rep2 Atha.Ws.sl.WT.YR.1.5h.postIR.5do_Rep1 Atha.Ws.sl.WT.YR.1.5h.postIR.5do_Rep2 Atha.Ws.sl.WT.YR.12h.postIR.5do_Rep1 Atha.Ws.sl.WT.YR.12h.postIR.5do_Rep2 Atha.Ws.sl.WT.YR.24h.postIR.6do_Rep1 Atha.Ws.sl.WT.YR.24h.postIR.6do_Rep2 Atha.Ws.sl.WT.YR.3h.postIR.5do_Rep1 Atha.Ws.sl.WT.YR.3h.postIR.5do_Rep2 Atha.Ws.sl.WT.YR.6h.postIR.5do_Rep1 Atha.Ws.sl.WT.YR.6h.postIR.5do_Rep2
Sample.Name Atha_Ws_sl_ATM1_HZE_1.5h-postIR_5do_Rep1 Atha_Ws_sl_ATM1_HZE_1.5h-postIR_5do_Rep2 Atha_Ws_sl_ATM1_HZE_12h-postIR_5do_Rep1 Atha_Ws_sl_ATM1_HZE_12h-postIR_5do_Rep2 Atha_Ws_sl_ATM1_HZE_24h-postIR_6do_Rep1 Atha_Ws_sl_ATM1_HZE_24h-postIR_6do_Rep2 Atha_Ws_sl_ATM1_HZE_6h-postIR_5do_Rep1 Atha_Ws_sl_ATM1_HZE_6h-postIR_5do_Rep2 Atha_Ws_sl_ATM1_noIR_CTRL_5do_Rep1 Atha_Ws_sl_ATM1_noIR_CTRL_5do_Rep2 Atha_Ws_sl_ATM1_noIR_CTRL_6do_Rep1 Atha_Ws_sl_ATM1_noIR_CTRL_6do_Rep2 Atha_Ws_sl_ATM1_YR_1.5h-postIR_5do_Rep1 Atha_Ws_sl_ATM1_YR_1.5h-postIR_5do_Rep2 Atha_Ws_sl_ATM1_YR_12h-postIR_5do_Rep1 Atha_Ws_sl_ATM1_YR_12h-postIR_5do_Rep2 Atha_Ws_sl_ATM1_YR_24h-postIR_6do_Rep1 Atha_Ws_sl_ATM1_YR_24h-postIR_6do_Rep2 Atha_Ws_sl_ATM1_YR_6h-postIR_5do_Rep1 Atha_Ws_sl_ATM1_YR_6h-postIR_5do_Rep2 Atha_Ws_sl_WT_HZE_1.5h-postIR_5do_Rep1 Atha_Ws_sl_WT_HZE_1.5h-postIR_5do_Rep2 Atha_Ws_sl_WT_HZE_12h-postIR_5do_Rep1 Atha_Ws_sl_WT_HZE_12h-postIR_5do_Rep2 Atha_Ws_sl_WT_HZE_24h-postIR_6do_Rep1 Atha_Ws_sl_WT_HZE_24h-postIR_6do_Rep2 Atha_Ws_sl_WT_HZE_3h-postIR_5do_Rep1 Atha_Ws_sl_WT_HZE_3h-postIR_5do_Rep2 Atha_Ws_sl_WT_HZE_6h-postIR_5do_Rep1 Atha_Ws_sl_WT_HZE_6h-postIR_5do_Rep2 Atha_Ws_sl_WT_noIR_CTRL_5do_Rep1 Atha_Ws_sl_WT_noIR_CTRL_5do_Rep2 Atha_Ws_sl_WT_noIR_CTRL_6do_Rep1 Atha_Ws_sl_WT_noIR_CTRL_6do_Rep2 Atha_Ws_sl_WT_YR_1.5h-postIR_5do_Rep1 Atha_Ws_sl_WT_YR_1.5h-postIR_5do_Rep2 Atha_Ws_sl_WT_YR_12h-postIR_5do_Rep1 Atha_Ws_sl_WT_YR_12h-postIR_5do_Rep2 Atha_Ws_sl_WT_YR_24h-postIR_6do_Rep1 Atha_Ws_sl_WT_YR_24h-postIR_6do_Rep2 Atha_Ws_sl_WT_YR_3h-postIR_5do_Rep1 Atha_Ws_sl_WT_YR_3h-postIR_5do_Rep2 Atha_Ws_sl_WT_YR_6h-postIR_5do_Rep1 Atha_Ws_sl_WT_YR_6h-postIR_5do_Rep2
GLDS 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46
Accession GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46 GLDS-46
Hardware Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish Petri dish
Tissue Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings Seedlings
Age 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days 5 days
Organism Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana
Ecotype WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0 WS-0
Genotype atm1-1 atm1-1 atm1-1 atm1-1 atm1-1 atm1-1 WT WT atm1-1 WT atm1-1 atm1-1 WT WT atm1-1 WT atm1-1 atm1-1 WT WT atm1-1 atm1-1 WT atm1-1 WT atm1-1 WT WT atm1-1 WT WT WT atm1-1 WT atm1-1 WT WT WT WT atm1-1 WT WT WT WT
Variety WS-0 atm1-1 WS-0 atm1-1 WS-0 atm1-1 WS-0 atm1-1 WS-0 atm1-1 WS-0 atm1-1 WS-0 WT WS-0 WT WS-0 atm1-1 WS-0 WT WS-0 atm1-1 WS-0 atm1-1 WS-0 WT WS-0 WT WS-0 atm1-1 WS-0 WT WS-0 atm1-1 WS-0 atm1-1 WS-0 WT WS-0 WT WS-0 atm1-1 WS-0 atm1-1 WS-0 WT WS-0 atm1-1 WS-0 WT WS-0 atm1-1 WS-0 WT WS-0 WT WS-0 atm1-1 WS-0 WT WS-0 WT WS-0 WT WS-0 atm1-1 WS-0 WT WS-0 atm1-1 WS-0 WT WS-0 WT WS-0 WT WS-0 WT WS-0 atm1-1 WS-0 WT WS-0 WT WS-0 WT WS-0 WT
Radiation GA6.0. GA1.5. HA1.5 HA12.0 HA24.0 GA1.5 GW12.0 HW12.0 GA6.0 GW6.0 GA12.0 GA24.0 HW12.0 GW3.0 HA24.0 GW12.0 GA24.0 HA1.5 HW6.0 HW1.5 UA UA GW24.0 UA.day.2 HW24.0 GA12.0 GW1.5 GW1.5 HA6.0 HW1.5 UW.day.2 HW3.0 HA12.0 HW3.0 HA6.0 HW6.0 HW24.0 GW6.0 UW UA UW GW3.0 GW24.0 UW
Gravity Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial Terrestrial
Developmental 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully. 5 day old seedling, cotyledons opened fully.
Time.series.or.Concentration.gradient 1.5h-postIR 1.5h-postIR 12h-postIR 12h-postIR 24h-postIR 24h-postIR 6h-postIR 6h-postIR NoIR NoIR NoIR NoIR 1.5h-postIR 1.5h-postIR 12h-postIR 12h-postIR 24h-postIR 24h-postIR 6h-postIR 6h-postIR 1.5h-postIR 1.5h-postIR 12h-postIR 12h-postIR 24h-postIR 24h-postIR 3h-postIR 3h-postIR 6h-postIR 6h-postIR NoIR NoIR NoIR NoIR 1.5h-postIR 1.5h-postIR 12h-postIR 12h-postIR 24h-postIR 24h-postIR 3h-postIR 3h-postIR 6h-postIR 6h-postIR
Light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light Cool white light
Assay..RNAseq. Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling
Temperature 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22 20-22
Treatment.type HZE HZE HZE HZE HZE HZE HZE HZE control control control control Gamma Gamma Gamma Gamma Gamma Gamma Gamma Gamma HZE HZE HZE HZE HZE HZE HZE HZE HZE HZE control control control control Gamma Gamma Gamma Gamma Gamma Gamma Gamma Gamma Gamma Gamma
Treatment.intensity GA6.0. GA1.5. HA1.5 HA12.0 HA24.0 GA1.5 GW12.0 HW12.0 GA6.0 GW6.0 GA12.0 GA24.0 HW12.0 GW3.0 HA24.0 GW12.0 GA24.0 HA1.5 HW6.0 HW1.5 UA UA GW24.0 UA.day.2 HW24.0 GA12.0 GW1.5 GW1.5 HA6.0 HW1.5 UW.day.2 HW3.0 HA12.0 HW3.0 HA6.0 HW6.0 HW24.0 GW6.0 UW UA UW GW3.0 GW24.0 UW
Treament.timing seedling_6h after Gamma irradiation_atm-1 seedling_1.5h after Gamma irradiation_atm-1 seedling_1.5h after HZE irradiation_atm-1 seedling_12h after HZE irradiation_atm-1 seedling_24h after HZE irradiation_atm-1 seedling_1.5h after Gamma irradiation_atm-1 seedling_12h after Gamma irradiation_WT seedling_12h after HZE irradiation_WT seedling_6h after Gamma irradiation_atm-1 seedling_6h after Gamma irradiation_WT seedling_12h after Gamma irradiation_atm-1 seedling_24h after Gamma irradiation_atm-1 seedling_12h after HZE irradiation_WT seedling_3h after Gamma irradiation_WT seedling_24h after HZE irradiation_atm-1 seedling_12h after Gamma irradiation_WT seedling_24h after Gamma irradiation_atm-1 seedling_1.5h after HZE irradiation_atm-1 seedling_6h after HZE irradiation_WT seedling_1.5h after HZE irradiation_WT seedling_day 1_unirradiated control_atm-1 seedling_day 1_unirradiated control_atm-1 seedling_24h after Gamma irradiation_WT seedling_day 2_unirradiated control_atm-1 seedling_24h after HZE irradiation_WT seedling_12h after Gamma irradiation_atm-1 seedling_1.5h after Gamma irradiation_WT seedling_1.5h after Gamma irradiation_WT seedling_6h after HZE irradiation_atm-1 seedling_1.5h after HZE irradiation_WT seedling_day 2_unirradiated control_WT seedling_3h after HZE irradiation_WT seedling_12h after HZE irradiation_atm-1 seedling_3h after HZE irradiation_WT seedling_6h after HZE irradiation_atm-1 seedling_6h after HZE irradiation_WT seedling_24h after HZE irradiation_WT seedling_6h after Gamma irradiation_WT seedling_day 2_unirradiated control_WT seedling_day 2_unirradiated control_atm-1 seedling_day 1_unirradiated control_WT seedling_3h after Gamma irradiation_WT seedling_24h after Gamma irradiation_WT seedling_day 1_unirradiated control_WT
Preservation.Method. NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 
 readData.out <- readData(inputFile) 
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
   kable( head(readData.out$data) ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
ATM1_HZE_15hpostIR_5do_Rep1 ATM1_HZE_15hpostIR_5do_Rep2 ATM1_HZE_12hpostIR_5do_Rep1 ATM1_HZE_12hpostIR_5do_Rep2 ATM1_HZE_24hpostIR_6do_Rep1 ATM1_HZE_24hpostIR_6do_Rep2 ATM1_HZE_6hpostIR_5do_Rep1 ATM1_HZE_6hpostIR_5do_Rep2 ATM1_noIR_CTRL_5do_Rep1 ATM1_noIR_CTRL_5do_Rep2 ATM1_noIR_CTRL_6do_Rep1 ATM1_noIR_CTRL_6do_Rep2 ATM1_YR_15hpostIR_5do_Rep1 ATM1_YR_15hpostIR_5do_Rep2 ATM1_YR_12hpostIR_5do_Rep1 ATM1_YR_12hpostIR_5do_Rep2 ATM1_YR_24hpostIR_6do_Rep1 ATM1_YR_24hpostIR_6do_Rep2 ATM1_YR_6hpostIR_5do_Rep1 ATM1_YR_6hpostIR_5do_Rep2 WT_HZE_15hpostIR_5do_Rep1 WT_HZE_15hpostIR_5do_Rep2 WT_HZE_12hpostIR_5do_Rep1 WT_HZE_12hpostIR_5do_Rep2 WT_HZE_24hpostIR_6do_Rep1 WT_HZE_24hpostIR_6do_Rep2 WT_HZE_3hpostIR_5do_Rep1 WT_HZE_3hpostIR_5do_Rep2 WT_HZE_6hpostIR_5do_Rep1 WT_HZE_6hpostIR_5do_Rep2 WT_noIR_CTRL_5do_Rep1 WT_noIR_CTRL_5do_Rep2 WT_noIR_CTRL_6do_Rep1 WT_noIR_CTRL_6do_Rep2 WT_YR_15hpostIR_5do_Rep1 WT_YR_15hpostIR_5do_Rep2 WT_YR_12hpostIR_5do_Rep1 WT_YR_12hpostIR_5do_Rep2 WT_YR_24hpostIR_6do_Rep1 WT_YR_24hpostIR_6do_Rep2 WT_YR_3hpostIR_5do_Rep1 WT_YR_3hpostIR_5do_Rep2 WT_YR_6hpostIR_5do_Rep1 WT_YR_6hpostIR_5do_Rep2
AT2G46830 3.700440 3.700440 3.000000 3.000000 3.807355 3.807355 3.321928 3.321928 3.807355 3.807355 3.807355 3.806773 3.807355 3.906891 3.000000 3.000000 3.906891 3.906891 3.599633 3.584963 3.700440 3.700440 3.000000 3.000000 3.700440 3.807355 3.584963 3.584963 3.169925 3.169925 3.906891 3.906891 3.807355 3.807355 3.906891 3.906891 3.000000 3.000000 3.906891 3.906891 3.807355 3.807355 3.700440 3.700440
AT4G21070 3.169925 3.169925 3.584963 3.584963 3.584963 3.459432 3.459432 3.459432 3.169925 3.000000 3.169925 3.169472 3.584963 3.584963 3.584963 3.584963 3.459432 3.459432 3.599633 3.584963 4.000000 4.000000 3.906891 3.906891 3.700440 3.700440 4.000000 4.000000 3.906891 3.906891 3.000000 3.000000 3.169925 3.169925 4.000000 4.000000 3.700440 3.700440 3.584963 3.584963 3.906891 3.906891 3.807355 3.807355
AT1G32900 3.584963 3.584963 3.169925 3.169925 3.700440 3.807355 3.700440 3.700440 3.906891 3.906891 3.807355 3.806773 4.000000 4.000000 3.000000 3.000000 4.000000 4.000000 3.599633 3.700440 3.584963 3.584963 3.169925 3.321928 3.700440 3.700440 3.459432 3.459432 3.584963 3.584963 3.906891 3.906891 3.700440 3.700440 4.000000 4.000000 3.169925 3.000000 4.000000 4.000000 3.906891 3.906891 3.700440 3.584963
AT3G02380 3.584963 3.584963 3.169925 3.169925 3.807355 3.807355 3.459432 3.321928 3.807355 3.807355 3.807355 3.806773 3.906891 3.906891 3.169925 3.169925 3.906891 3.906891 3.715671 3.700440 3.584963 3.584963 3.169925 3.169925 3.807355 3.807355 3.321928 3.321928 3.169925 3.169925 3.906891 3.906891 3.807355 3.807355 3.906891 3.906891 3.169925 3.169925 3.807355 3.906891 3.807355 3.807355 3.700440 3.700440
AT1G10070 4.000000 4.000000 3.169925 3.321928 3.321928 3.459432 3.459432 3.459432 3.321928 3.321928 3.459432 3.458913 3.169925 3.169925 3.169925 3.459432 3.169925 3.169925 3.182161 3.321928 4.000000 4.000000 3.169925 3.169925 3.169925 3.321928 4.000000 4.000000 3.321928 3.459432 3.169925 3.169925 3.459432 3.321928 3.169925 3.169925 3.169925 3.169925 3.169925 3.169925 3.169925 3.321928 3.169925 3.459432
AT5G48720 3.459432 3.459432 3.700440 3.700440 3.584963 3.584963 3.584963 3.584963 3.321928 3.321928 3.321928 3.321439 3.584963 3.700440 3.700440 3.700440 3.584963 3.584963 3.599633 3.700440 4.087463 4.087463 4.000000 3.906891 3.807355 3.807355 4.000000 4.000000 4.000000 4.000000 3.321928 3.321928 3.321928 3.321928 4.087463 4.087463 3.807355 3.807355 3.700440 3.700440 4.000000 4.000000 3.906891 3.906891 
 readSampleInfo.out <- readSampleInfo(sampleInfoFile) 
 kable( readSampleInfo.out ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Genotype Radiation Time.series.or.Concentration.gradient Treatment.type Treatment.intensity Treament.timing
ATM1_HZE_15hpostIR_5do_Rep1 Genotypeatm11 RadiationGA60 Time.series.or.Concentration.gradient15hpostIR Treatment.typeHZE Treatment.intensityGA60 Treament.timingseedling_6h after Gamma irradiation_atm1
ATM1_HZE_15hpostIR_5do_Rep2 Genotypeatm11 RadiationGA15 Time.series.or.Concentration.gradient15hpostIR Treatment.typeHZE Treatment.intensityGA15 Treament.timingseedling_15h after Gamma irradiation_atm1
ATM1_HZE_12hpostIR_5do_Rep1 Genotypeatm11 RadiationHA15 Time.series.or.Concentration.gradient12hpostIR Treatment.typeHZE Treatment.intensityHA15 Treament.timingseedling_15h after HZE irradiation_atm1
ATM1_HZE_12hpostIR_5do_Rep2 Genotypeatm11 RadiationHA120 Time.series.or.Concentration.gradient12hpostIR Treatment.typeHZE Treatment.intensityHA120 Treament.timingseedling_12h after HZE irradiation_atm1
ATM1_HZE_24hpostIR_6do_Rep1 Genotypeatm11 RadiationHA240 Time.series.or.Concentration.gradient24hpostIR Treatment.typeHZE Treatment.intensityHA240 Treament.timingseedling_24h after HZE irradiation_atm1
ATM1_HZE_24hpostIR_6do_Rep2 Genotypeatm11 RadiationGA15 Time.series.or.Concentration.gradient24hpostIR Treatment.typeHZE Treatment.intensityGA15 Treament.timingseedling_15h after Gamma irradiation_atm1
ATM1_HZE_6hpostIR_5do_Rep1 GenotypeWT RadiationGW120 Time.series.or.Concentration.gradient6hpostIR Treatment.typeHZE Treatment.intensityGW120 Treament.timingseedling_12h after Gamma irradiation_WT
ATM1_HZE_6hpostIR_5do_Rep2 GenotypeWT RadiationHW120 Time.series.or.Concentration.gradient6hpostIR Treatment.typeHZE Treatment.intensityHW120 Treament.timingseedling_12h after HZE irradiation_WT
ATM1_noIR_CTRL_5do_Rep1 Genotypeatm11 RadiationGA60 Time.series.or.Concentration.gradientNoIR Treatment.typecontrol Treatment.intensityGA60 Treament.timingseedling_6h after Gamma irradiation_atm1
ATM1_noIR_CTRL_5do_Rep2 GenotypeWT RadiationGW60 Time.series.or.Concentration.gradientNoIR Treatment.typecontrol Treatment.intensityGW60 Treament.timingseedling_6h after Gamma irradiation_WT
ATM1_noIR_CTRL_6do_Rep1 Genotypeatm11 RadiationGA120 Time.series.or.Concentration.gradientNoIR Treatment.typecontrol Treatment.intensityGA120 Treament.timingseedling_12h after Gamma irradiation_atm1
ATM1_noIR_CTRL_6do_Rep2 Genotypeatm11 RadiationGA240 Time.series.or.Concentration.gradientNoIR Treatment.typecontrol Treatment.intensityGA240 Treament.timingseedling_24h after Gamma irradiation_atm1
ATM1_YR_15hpostIR_5do_Rep1 GenotypeWT RadiationHW120 Time.series.or.Concentration.gradient15hpostIR Treatment.typeGamma Treatment.intensityHW120 Treament.timingseedling_12h after HZE irradiation_WT
ATM1_YR_15hpostIR_5do_Rep2 GenotypeWT RadiationGW30 Time.series.or.Concentration.gradient15hpostIR Treatment.typeGamma Treatment.intensityGW30 Treament.timingseedling_3h after Gamma irradiation_WT
ATM1_YR_12hpostIR_5do_Rep1 Genotypeatm11 RadiationHA240 Time.series.or.Concentration.gradient12hpostIR Treatment.typeGamma Treatment.intensityHA240 Treament.timingseedling_24h after HZE irradiation_atm1
ATM1_YR_12hpostIR_5do_Rep2 GenotypeWT RadiationGW120 Time.series.or.Concentration.gradient12hpostIR Treatment.typeGamma Treatment.intensityGW120 Treament.timingseedling_12h after Gamma irradiation_WT
ATM1_YR_24hpostIR_6do_Rep1 Genotypeatm11 RadiationGA240 Time.series.or.Concentration.gradient24hpostIR Treatment.typeGamma Treatment.intensityGA240 Treament.timingseedling_24h after Gamma irradiation_atm1
ATM1_YR_24hpostIR_6do_Rep2 Genotypeatm11 RadiationHA15 Time.series.or.Concentration.gradient24hpostIR Treatment.typeGamma Treatment.intensityHA15 Treament.timingseedling_15h after HZE irradiation_atm1
ATM1_YR_6hpostIR_5do_Rep1 GenotypeWT RadiationHW60 Time.series.or.Concentration.gradient6hpostIR Treatment.typeGamma Treatment.intensityHW60 Treament.timingseedling_6h after HZE irradiation_WT
ATM1_YR_6hpostIR_5do_Rep2 GenotypeWT RadiationHW15 Time.series.or.Concentration.gradient6hpostIR Treatment.typeGamma Treatment.intensityHW15 Treament.timingseedling_15h after HZE irradiation_WT
WT_HZE_15hpostIR_5do_Rep1 Genotypeatm11 RadiationUA Time.series.or.Concentration.gradient15hpostIR Treatment.typeHZE Treatment.intensityUA Treament.timingseedling_day 1_unirradiated control_atm1
WT_HZE_15hpostIR_5do_Rep2 Genotypeatm11 RadiationUA Time.series.or.Concentration.gradient15hpostIR Treatment.typeHZE Treatment.intensityUA Treament.timingseedling_day 1_unirradiated control_atm1
WT_HZE_12hpostIR_5do_Rep1 GenotypeWT RadiationGW240 Time.series.or.Concentration.gradient12hpostIR Treatment.typeHZE Treatment.intensityGW240 Treament.timingseedling_24h after Gamma irradiation_WT
WT_HZE_12hpostIR_5do_Rep2 Genotypeatm11 RadiationUAday2 Time.series.or.Concentration.gradient12hpostIR Treatment.typeHZE Treatment.intensityUAday2 Treament.timingseedling_day 2_unirradiated control_atm1
WT_HZE_24hpostIR_6do_Rep1 GenotypeWT RadiationHW240 Time.series.or.Concentration.gradient24hpostIR Treatment.typeHZE Treatment.intensityHW240 Treament.timingseedling_24h after HZE irradiation_WT
WT_HZE_24hpostIR_6do_Rep2 Genotypeatm11 RadiationGA120 Time.series.or.Concentration.gradient24hpostIR Treatment.typeHZE Treatment.intensityGA120 Treament.timingseedling_12h after Gamma irradiation_atm1
WT_HZE_3hpostIR_5do_Rep1 GenotypeWT RadiationGW15 Time.series.or.Concentration.gradient3hpostIR Treatment.typeHZE Treatment.intensityGW15 Treament.timingseedling_15h after Gamma irradiation_WT
WT_HZE_3hpostIR_5do_Rep2 GenotypeWT RadiationGW15 Time.series.or.Concentration.gradient3hpostIR Treatment.typeHZE Treatment.intensityGW15 Treament.timingseedling_15h after Gamma irradiation_WT
WT_HZE_6hpostIR_5do_Rep1 Genotypeatm11 RadiationHA60 Time.series.or.Concentration.gradient6hpostIR Treatment.typeHZE Treatment.intensityHA60 Treament.timingseedling_6h after HZE irradiation_atm1
WT_HZE_6hpostIR_5do_Rep2 GenotypeWT RadiationHW15 Time.series.or.Concentration.gradient6hpostIR Treatment.typeHZE Treatment.intensityHW15 Treament.timingseedling_15h after HZE irradiation_WT
WT_noIR_CTRL_5do_Rep1 GenotypeWT RadiationUWday2 Time.series.or.Concentration.gradientNoIR Treatment.typecontrol Treatment.intensityUWday2 Treament.timingseedling_day 2_unirradiated control_WT
WT_noIR_CTRL_5do_Rep2 GenotypeWT RadiationHW30 Time.series.or.Concentration.gradientNoIR Treatment.typecontrol Treatment.intensityHW30 Treament.timingseedling_3h after HZE irradiation_WT
WT_noIR_CTRL_6do_Rep1 Genotypeatm11 RadiationHA120 Time.series.or.Concentration.gradientNoIR Treatment.typecontrol Treatment.intensityHA120 Treament.timingseedling_12h after HZE irradiation_atm1
WT_noIR_CTRL_6do_Rep2 GenotypeWT RadiationHW30 Time.series.or.Concentration.gradientNoIR Treatment.typecontrol Treatment.intensityHW30 Treament.timingseedling_3h after HZE irradiation_WT
WT_YR_15hpostIR_5do_Rep1 Genotypeatm11 RadiationHA60 Time.series.or.Concentration.gradient15hpostIR Treatment.typeGamma Treatment.intensityHA60 Treament.timingseedling_6h after HZE irradiation_atm1
WT_YR_15hpostIR_5do_Rep2 GenotypeWT RadiationHW60 Time.series.or.Concentration.gradient15hpostIR Treatment.typeGamma Treatment.intensityHW60 Treament.timingseedling_6h after HZE irradiation_WT
WT_YR_12hpostIR_5do_Rep1 GenotypeWT RadiationHW240 Time.series.or.Concentration.gradient12hpostIR Treatment.typeGamma Treatment.intensityHW240 Treament.timingseedling_24h after HZE irradiation_WT
WT_YR_12hpostIR_5do_Rep2 GenotypeWT RadiationGW60 Time.series.or.Concentration.gradient12hpostIR Treatment.typeGamma Treatment.intensityGW60 Treament.timingseedling_6h after Gamma irradiation_WT
WT_YR_24hpostIR_6do_Rep1 GenotypeWT RadiationUW Time.series.or.Concentration.gradient24hpostIR Treatment.typeGamma Treatment.intensityUW Treament.timingseedling_day 2_unirradiated control_WT
WT_YR_24hpostIR_6do_Rep2 Genotypeatm11 RadiationUA Time.series.or.Concentration.gradient24hpostIR Treatment.typeGamma Treatment.intensityUA Treament.timingseedling_day 2_unirradiated control_atm1
WT_YR_3hpostIR_5do_Rep1 GenotypeWT RadiationUW Time.series.or.Concentration.gradient3hpostIR Treatment.typeGamma Treatment.intensityUW Treament.timingseedling_day 1_unirradiated control_WT
WT_YR_3hpostIR_5do_Rep2 GenotypeWT RadiationGW30 Time.series.or.Concentration.gradient3hpostIR Treatment.typeGamma Treatment.intensityGW30 Treament.timingseedling_3h after Gamma irradiation_WT
WT_YR_6hpostIR_5do_Rep1 GenotypeWT RadiationGW240 Time.series.or.Concentration.gradient6hpostIR Treatment.typeGamma Treatment.intensityGW240 Treament.timingseedling_24h after Gamma irradiation_WT
WT_YR_6hpostIR_5do_Rep2 GenotypeWT RadiationUW Time.series.or.Concentration.gradient6hpostIR Treatment.typeGamma Treatment.intensityUW Treament.timingseedling_day 1_unirradiated control_WT 
 input_selectOrg ="NEW" 
 input_selectGO <- 'GOBP'   #Gene set category 
 input_noIDConversion = TRUE  
 allGeneInfo.out <- geneInfo(geneInfoFile) 
 converted.out = NULL 
 convertedData.out <- convertedData()    
 nGenesFilter()  
## [1] "16156 genes in 44 samples. 16156  genes passed filter.\n Original gene IDs used."
 convertedCounts.out <- convertedCounts()  # converted counts, just for compatibility

2. Pre-process

# Read counts per library 
 parDefault = par() 
 par(mar=c(12,4,2,2)) 
 # barplot of total read counts
 x <- readData.out$rawCounts
 groups = as.factor( detectGroups(colnames(x ) ) )
 if(nlevels(groups)<=1 | nlevels(groups) >20 )  
  col1 = 'green'  else
  col1 = rainbow(nlevels(groups))[ groups ]             
         
 barplot( colSums(x)/1e6, 
        col=col1,las=3, main="Total read counts (millions)")  

 readCountsBias()  # detecting bias in sequencing depth 
## [1] 0.1982081
## [1] 0.9367154
## [1] 0.5100945
## [1] 0.6402451
## [1] 0.2308868
## [1] 0.5100945
## [1] 0.527478
## [1] "No bias detected"
 # Box plot 
 x = readData.out$data 
 boxplot(x, las = 2, col=col1,
    ylab='Transformed expression levels',
    main='Distribution of transformed data') 

 #Density plot 
 par(parDefault) 
## Warning in par(parDefault): graphical parameter "cin" cannot be set
## Warning in par(parDefault): graphical parameter "cra" cannot be set
## Warning in par(parDefault): graphical parameter "csi" cannot be set
## Warning in par(parDefault): graphical parameter "cxy" cannot be set
## Warning in par(parDefault): graphical parameter "din" cannot be set
## Warning in par(parDefault): graphical parameter "page" cannot be set
 densityPlot()       

 # Scatter plot of the first two samples 
 plot(x[,1:2],xlab=colnames(x)[1],ylab=colnames(x)[2], 
    main='Scatter plot of first two samples') 

 ####plot gene or gene family
 input_selectOrg ="BestMatch" 
 input_geneSearch <- 'HOXA' #Gene ID for searching 
 genePlot()  
## NULL
 input_useSD <- 'FALSE' #Use standard deviation instead of standard error in error bar? 
 geneBarPlotError()       
## NULL

3. Heatmap

 # hierarchical clustering tree
 x <- readData.out$data
 maxGene <- apply(x,1,max)
 # remove bottom 25% lowly expressed genes, which inflate the PPC
 x <- x[which(maxGene > quantile(maxGene)[1] ) ,] 
 plot(as.dendrogram(hclust2( dist2(t(x)))), ylab="1 - Pearson C.C.", type = "rectangle") 

 #Correlation matrix
 input_labelPCC <- TRUE #Show correlation coefficient? 
 correlationMatrix() 

 # Parameters for heatmap
 input_nGenes <- 1000   #Top genes for heatmap
 input_geneCentering <- TRUE    #centering genes ?
 input_sampleCentering <- FALSE #Center by sample?
 input_geneNormalize <- FALSE   #Normalize by gene?
 input_sampleNormalize <- FALSE #Normalize by sample?
 input_noSampleClustering <- FALSE  #Use original sample order
 input_heatmapCutoff <- 4   #Remove outliers beyond number of SDs 
 input_distFunctions <- 1   #which distant funciton to use
 input_hclustFunctions <- 1 #Linkage type
 input_heatColors1 <- 1 #Colors
 input_selectFactorsHeatmap <- 'Treatment.type' #Sample coloring factors 
 png('heatmap.png', width = 10, height = 15, units = 'in', res = 300) 
 staticHeatmap() 
 dev.off()  
## png 
##   2

[heatmap] (heatmap.png)

 heatmapPlotly() # interactive heatmap using Plotly

4. K-means clustering

 input_nGenesKNN <- 2000    #Number of genes fro k-Means
 input_nClusters <- 4   #Number of clusters 
 maxGeneClustering = 12000
 input_kmeansNormalization <- 'geneMean'    #Normalization
 input_KmeansReRun <- 0 #Random seed 

 distributionSD()  #Distribution of standard deviations 

 KmeansNclusters()  #Number of clusters 

 Kmeans.out = Kmeans()   #Running K-means 
 KmeansHeatmap()   #Heatmap for k-Means 

 #Read gene sets for enrichment analysis 
 sqlite  <- dbDriver('SQLite')
 input_selectGO3 <- 'GOBP'  #Gene set category
 input_minSetSize <- 15 #Min gene set size
 input_maxSetSize <- 2000   #Max gene set size 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO3,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 # Alternatively, users can use their own GMT files by
 #GeneSets.out <- readGMTRobust('somefile.GMT')  
 results <- KmeansGO()  #Enrichment analysis for k-Means clusters   
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 1.35e-29 81 Response to abiotic stimulus
3.16e-23 44 Response to light stimulus
1.07e-22 44 Response to radiation
3.39e-14 21 Response to red or far red light
4.95e-12 47 Response to oxygen-containing compound
1.12e-11 45 Cellular response to chemical stimulus
5.04e-11 46 Response to hormone
8.06e-11 46 Response to endogenous stimulus
1.44e-10 50 Response to organic substance
2.63e-10 33 Response to inorganic substance
B 7.89e-29 53 Secondary metabolic process
2.07e-25 118 Small molecule metabolic process
6.47e-25 129 Response to abiotic stimulus
5.15e-24 34 Secondary metabolite biosynthetic process
3.74e-21 86 Oxoacid metabolic process
5.70e-21 86 Organic acid metabolic process
4.84e-18 28 Response to karrikin
8.22e-18 44 Sulfur compound metabolic process
8.22e-18 31 Sulfur compound biosynthetic process
8.26e-17 89 Oxidation-reduction process
C 1.59e-25 106 Response to abiotic stimulus
5.67e-23 84 Cellular response to chemical stimulus
3.03e-18 89 Response to organic substance
2.37e-17 29 Cellular response to decreased oxygen levels
2.37e-17 29 Cellular response to oxygen levels
1.68e-16 28 Cellular response to hypoxia
1.68e-16 75 Response to oxygen-containing compound
3.77e-16 75 Catabolic process
4.95e-16 29 Response to decreased oxygen levels
5.13e-16 29 Response to oxygen levels
D 2.43e-56 136 Response to abiotic stimulus
5.50e-50 44 Response to chitin
2.01e-44 106 Response to oxygen-containing compound
2.06e-44 48 Cellular response to decreased oxygen levels
2.06e-44 48 Cellular response to oxygen levels
2.06e-44 48 Cellular response to hypoxia
1.14e-42 101 Cellular response to chemical stimulus
6.03e-42 48 Response to hypoxia
1.18e-41 48 Response to decreased oxygen levels
1.25e-41 90 Cellular response to stress 
 input_seedTSNE <- 0    #Random seed for t-SNE
 input_colorGenes <- TRUE   #Color genes in t-SNE plot? 
 tSNEgenePlot()  #Plot genes using t-SNE

5. PCA and beyond

 input_selectFactors <- 'Radiation' #Factor coded by color
 input_selectFactors2 <- 'Genotype' #Factor coded by shape
 input_tsneSeed2 <- 0   #Random seed for t-SNE 
 #PCA, MDS and t-SNE plots
 PCAplot()  

 MDSplot() 

 tSNEplot()  

 #Read gene sets for pathway analysis using PGSEA on principal components 
 input_selectGO6 <- 'GOBP' 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO6,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 PCApathway() # Run PGSEA analysis 
## Warning: Package 'KEGG.db' is deprecated and will be removed from Bioconductor
##   version 3.12

 cat( PCA2factor() )   #The correlation between PCs with factors 
## 
##  Correlation between Principal Components (PCs) with factors
## PC1 is correlated with Treatment.type (p=6.94e-04).
## PC2 is correlated with Time.series.or.Concentration.gradient (p=3.65e-13).
## PC3 is correlated with Time.series.or.Concentration.gradient (p=3.49e-04).

6. DEG1

 input_CountsDEGMethod <- 2 #DESeq2= 3,limma-voom=2,limma-trend=1 
 input_limmaPval <- 0.1 #FDR cutoff
 input_limmaFC <- 2 #Fold-change cutoff
 input_selectModelComprions <- c('Treatment.type: Treatment.typecontrol vs. Treatment.typeGamma','Treatment.type: Treatment.typecontrol vs. Treatment.typeHZE') #Selected comparisons
 input_selectFactorsModel <- 'Treatment.type'   #Selected comparisons
 input_selectInteractions <- NULL   #Selected comparisons
 input_selectBlockFactorsModel <- NULL  #Selected comparisons
 factorReferenceLevels.out <- c('Treatment.type:Treatment.typeHZE') 
 
 limma.out <- limma()
 DEG.data.out <- DEG.data()
 limma.out$comparisons 
## [1] "Treatment.typecontrol-Treatment.typeGamma"
## [2] "Treatment.typecontrol-Treatment.typeHZE"
 input_selectComparisonsVenn = limma.out$comparisons[1:3] # use first three comparisons
 input_UpDownRegulated <- FALSE #Split up and down regulated genes 
 vennPlot() # Venn diagram 

  sigGeneStats() # number of DEGs as figure 

  sigGeneStatsTable() # number of DEGs as table 
##                                                                         Comparisons
## Treatment.typecontrol-Treatment.typeGamma Treatment.typecontrol-Treatment.typeGamma
## Treatment.typecontrol-Treatment.typeHZE     Treatment.typecontrol-Treatment.typeHZE
##                                           Up Down
## Treatment.typecontrol-Treatment.typeGamma  0    0
## Treatment.typecontrol-Treatment.typeHZE    0    0

7. DEG2

 input_selectContrast = limma.out$comparisons[1] # use first  comparisons 
 selectedHeatmap.data.out <- selectedHeatmap.data()
 selectedHeatmap()   # heatmap for DEGs in selected comparison
## Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : 'data' must be of a vector type, was 'NULL'
 # Save gene lists and data into files
 write.csv( selectedHeatmap.data()$genes, 'heatmap.data.csv') 
 write.csv(DEG.data(),'DEG.data.csv' )
 write(AllGeneListsGMT() ,'AllGeneListsGMT.gmt')
 input_selectGO2 <- 'GOBP'  #Gene set category 
 geneListData.out <- geneListData()  
 volcanoPlot()  

  scatterPlot()  
## Error in findContrastSamples(input_selectContrast, colnames(convertedData.out), : object 'c.out' not found
  MAplot()  
## Error in findContrastSamples(input_selectContrast, colnames(convertedData.out), : object 'c.out' not found
  geneListGOTable.out <- geneListGOTable()  
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO2,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_removeRedudantSets <- TRUE   #Remove highly redundant gene sets? 
 results <- geneListGO()  #Enrichment analysis
## Error in if (dim(results1)[2] == 1) return(results1) else {: argument is of length zero
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 1.35e-29 81 Response to abiotic stimulus
3.16e-23 44 Response to light stimulus
1.07e-22 44 Response to radiation
3.39e-14 21 Response to red or far red light
4.95e-12 47 Response to oxygen-containing compound
1.12e-11 45 Cellular response to chemical stimulus
5.04e-11 46 Response to hormone
8.06e-11 46 Response to endogenous stimulus
1.44e-10 50 Response to organic substance
2.63e-10 33 Response to inorganic substance
B 7.89e-29 53 Secondary metabolic process
2.07e-25 118 Small molecule metabolic process
6.47e-25 129 Response to abiotic stimulus
5.15e-24 34 Secondary metabolite biosynthetic process
3.74e-21 86 Oxoacid metabolic process
5.70e-21 86 Organic acid metabolic process
4.84e-18 28 Response to karrikin
8.22e-18 44 Sulfur compound metabolic process
8.22e-18 31 Sulfur compound biosynthetic process
8.26e-17 89 Oxidation-reduction process
C 1.59e-25 106 Response to abiotic stimulus
5.67e-23 84 Cellular response to chemical stimulus
3.03e-18 89 Response to organic substance
2.37e-17 29 Cellular response to decreased oxygen levels
2.37e-17 29 Cellular response to oxygen levels
1.68e-16 28 Cellular response to hypoxia
1.68e-16 75 Response to oxygen-containing compound
3.77e-16 75 Catabolic process
4.95e-16 29 Response to decreased oxygen levels
5.13e-16 29 Response to oxygen levels
D 2.43e-56 136 Response to abiotic stimulus
5.50e-50 44 Response to chitin
2.01e-44 106 Response to oxygen-containing compound
2.06e-44 48 Cellular response to decreased oxygen levels
2.06e-44 48 Cellular response to oxygen levels
2.06e-44 48 Cellular response to hypoxia
1.14e-42 101 Cellular response to chemical stimulus
6.03e-42 48 Response to hypoxia
1.18e-41 48 Response to decreased oxygen levels
1.25e-41 90 Cellular response to stress

STRING-db API access. We need to find the taxonomy id of your species, this used by STRING. First we try to guess the ID based on iDEP’s database. Users can also skip this step and assign NCBI taxonomy id directly by findTaxonomyID.out = 10090 # mouse 10090, human 9606 etc.

 STRING10_species = read.csv(STRING10_speciesFile)  
 ix = grep('Arabidopsis thaliana', STRING10_species$official_name ) 
 findTaxonomyID.out <- STRING10_species[ix,1] # find taxonomyID
 findTaxonomyID.out  
## [1] 3702

Enrichment analysis using STRING

  STRINGdb_geneList.out <- STRINGdb_geneList() #convert gene lists
## Error in names(x) <- value: 'names' attribute [2] must be the same length as the vector [1]
 input_STRINGdbGO <- 'Process'  #'Process', 'Component', 'Function', 'KEGG', 'Pfam', 'InterPro' 
 results <- stringDB_GO_enrichmentData()  # enrichment using STRING 
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
x
NULL

PPI network retrieval and analysis

 input_nGenesPPI <- 100 #Number of top genes for PPI retrieval and analysis 
 stringDB_network1(1) #Show PPI network 
## Error in stringDB_network1(1): object 'STRINGdb_geneList.out' not found

Generating interactive PPI

 write(stringDB_network_link(), 'PPI_results.html') # write results to html file 
## Error in stringDB_network_link(): object 'STRINGdb_geneList.out' not found
 browseURL('PPI_results.html') # open in browser

8. Pathway analysis

 input_selectContrast1 = limma.out$comparisons[1] 
 #input_selectContrast1 = limma.out$comparisons[3] # manually set
 input_selectGO <- 'GOBP'   #Gene set category 
 #input_selectGO='custom' # if custom gmt file
 input_minSetSize <- 15 #Min size for gene set
 input_maxSetSize <- 2000   #Max size for gene set 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_pathwayPvalCutoff <- 0.2 #FDR cutoff
 input_nPathwayShow <- 30   #Top pathways to show
 input_absoluteFold <- FALSE    #Use absolute values of fold-change?
 input_GenePvalCutoff <- 1  #FDR to remove genes 

 input_pathwayMethod = 1  # 1  GAGE
 gagePathwayData.out <- gagePathwayData()  # pathway analysis using GAGE  
   
 results <- gagePathwayData.out  #Enrichment analysis for k-Means clusters  
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Direction GAGE analysis: Treatment.typecontrol vs Treatment.typeGamma statistic Genes adj.Pval
Down Ribosome biogenesis -5.4049 343 8.9e-05
Ribonucleoprotein complex biogenesis -5.141 438 1.6e-04
NcRNA metabolic process -4.7065 425 9.4e-04
RRNA metabolic process -4.599 244 1.3e-03
RRNA processing -4.4451 239 2.1e-03
NcRNA processing -4.2518 357 3.5e-03
DNA repair -4.2439 314 3.5e-03
Cellular response to DNA damage stimulus -4.1582 337 4.4e-03
DNA replication -3.8322 162 1.6e-02
Double-strand break repair -3.726 118 2.5e-02
RNA modification -3.5134 321 4.1e-02
DNA-dependent DNA replication -3.4959 133 4.4e-02
Energy derivation by oxidation of organic compounds -3.3904 130 5.8e-02
DNA unwinding involved in DNA replication -3.3888 18 8.2e-02
Glycogen metabolic process -3.3484 22 7.4e-02
Energy reserve metabolic process -3.3484 22 7.4e-02
Protein targeting to mitochondrion -3.3157 57 6.7e-02
Protein localization to mitochondrion -3.3124 62 6.7e-02
Establishment of protein localization to mitochondrion -3.3124 62 6.7e-02
Sulfur compound metabolic process -3.3022 319 6.7e-02
Double-strand break repair via homologous recombination -3.2904 78 6.7e-02
Response to toxic substance -3.2335 275 6.7e-02
DNA recombination -3.17 151 7.4e-02
Protein import -3.1549 131 7.4e-02
Recombinational repair -3.0691 83 1.0e-01
DNA geometric change -3.0136 59 1.0e-01
DNA duplex unwinding -3.0136 59 1.0e-01
Protein transmembrane transport -2.9981 112 1.0e-01
Sulfur compound biosynthetic process -2.9951 146 1.0e-01
Mitochondrion organization -2.975 151 1.0e-01 
 pathwayListData.out = pathwayListData() 
 enrichmentPlot(pathwayListData.out, 25  ) 

  enrichmentNetwork(pathwayListData.out )  

  enrichmentNetworkPlotly(pathwayListData.out) 

## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
 input_pathwayMethod = 3  # 1  fgsea 
 fgseaPathwayData.out <- fgseaPathwayData() #Pathway analysis using fgsea 
## Warning in fgsea(pathways = gmt, stats = fold, minSize = input_minSetSize, :
## You are trying to run fgseaSimple. It is recommended to use fgseaMultilevel. To
## run fgseaMultilevel, you need to remove the nperm argument in the fgsea function
## call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (21.57% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
 results <- fgseaPathwayData.out  #Enrichment analysis for k-Means clusters 
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Direction GSEA analysis: Treatment.typecontrol vs Treatment.typeGamma NES Genes adj.Pval
Down Response to ionizing radiation -2.4119 27 7.9e-03
Double-strand break repair -2.3506 118 7.9e-03
Double-strand break repair via homologous recombination -2.2255 78 7.9e-03
Recombinational repair -2.1395 83 7.9e-03
Starch catabolic process -2.0949 15 7.9e-03
DNA repair -2.0816 314 7.9e-03
Reciprocal meiotic recombination -2.0733 46 7.9e-03
Homologous recombination -2.0733 46 7.9e-03
DNA unwinding involved in DNA replication -2.0675 18 1.2e-02
DNA recombination -2.038 151 7.9e-03
Cellular response to DNA damage stimulus -2.0326 337 7.9e-03
DNA replication -2.0307 162 7.9e-03
DNA-dependent DNA replication -2.0208 133 7.9e-03
DNA geometric change -2.0193 59 7.9e-03
DNA duplex unwinding -2.0193 59 7.9e-03
Ribosome biogenesis -1.9984 343 7.9e-03
DNA biosynthetic process -1.9908 57 7.9e-03
Meiosis I -1.9892 56 7.9e-03
Protein targeting to mitochondrion -1.9849 57 7.9e-03
Homologous chromosome segregation -1.9839 26 7.9e-03
Synapsis -1.9771 20 1.2e-02
Glycogen metabolic process -1.9602 22 1.2e-02
Energy reserve metabolic process -1.9602 22 1.2e-02
Toxin catabolic process -1.9598 35 1.4e-02
Protein localization to mitochondrion -1.9522 62 1.4e-02
Establishment of protein localization to mitochondrion -1.9522 62 1.4e-02
Meiosis I cell cycle process -1.9494 62 1.4e-02
Regulation of sulfur metabolic process -1.9305 28 1.2e-02
S-glycoside biosynthetic process -1.9286 37 1.7e-02
Up Branched-chain amino acid catabolic process 2.0016 16 7.9e-03 
  pathwayListData.out = pathwayListData() 
 enrichmentPlot(pathwayListData.out, 25  ) 

  enrichmentNetwork(pathwayListData.out )  

  enrichmentNetworkPlotly(pathwayListData.out) 

   PGSEAplot() # pathway analysis using PGSEA 
## Error in findContrastSamples(input_selectContrast1, colnames(convertedData.out), : object 'c.out' not found

9. Chromosome

 input_selectContrast2 = limma.out$comparisons[1] 
 #input_selectContrast2 = limma.out$comparisons[3] # manually set
 input_limmaPvalViz <- 0.1  #FDR to filter genes
 input_limmaFCViz <- 2  #FDR to filter genes 
 genomePlotly() # shows fold-changes on the genome 
## Warning in eval(quote(list(...)), env): NAs introduced by coercion
## Warning in genomePlotly(): NAs introduced by coercion

10. Biclustering

 input_nGenesBiclust <- 1000    #Top genes for biclustering
 input_biclustMethod <- 'BCCC()'    #Method: 'BCCC', 'QUBIC', 'runibic' ... 
 biclustering.out = biclustering()  # run analysis

 input_selectBicluster <- 1 #select a cluster 
 biclustHeatmap()   # heatmap for selected cluster 

 input_selectGO4 <- 'GOBP'  #Gene set category 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO4,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 results <- geneListBclustGO()  #Enrichment analysis for k-Means clusters   
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
adj.Pval Genes Pathways
5.3e-93 260 Response to abiotic stimulus
1.1e-51 173 Response to oxygen-containing compound
4.1e-49 146 Cellular response to stress
5.1e-47 161 Cellular response to chemical stimulus
5.3e-46 185 Response to organic substance
3.6e-45 63 Cellular response to hypoxia
4.1e-45 63 Cellular response to decreased oxygen levels
4.1e-45 63 Cellular response to oxygen levels
1.1e-41 63 Response to hypoxia
2.8e-41 63 Response to decreased oxygen levels

11. Co-expression network

 input_mySoftPower <- 5 #SoftPower to cutoff
 input_nGenesNetwork <- 1000    #Number of top genes
 input_minModuleSize <- 20  #Module size minimum 
 wgcna.out = wgcna()   # run WGCNA  
## Warning: executing %dopar% sequentially: no parallel backend registered
##    Power SFT.R.sq   slope truncated.R.sq mean.k. median.k. max.k.
## 1      1 0.720000  1.5400          0.872  373.00    385.00  521.0
## 2      2 0.219000  0.4450          0.727  201.00    194.00  352.0
## 3      3 0.000261 -0.0124          0.692  125.00    113.00  262.0
## 4      4 0.192000 -0.3440          0.729   85.10     73.20  204.0
## 5      5 0.425000 -0.6040          0.808   60.70     51.00  164.0
## 6      6 0.596000 -0.7820          0.877   44.90     37.00  134.0
## 7      7 0.688000 -0.9280          0.925   34.10     27.50  111.0
## 8      8 0.736000 -1.0400          0.940   26.40     20.70   93.5
## 9      9 0.754000 -1.1500          0.951   20.80     15.90   79.3
## 10    10 0.769000 -1.2100          0.967   16.60     12.30   67.8
## 11    12 0.810000 -1.3200          0.975   11.00      7.69   50.5
## 12    14 0.805000 -1.4100          0.975    7.52      5.03   38.4
## 13    16 0.813000 -1.4700          0.963    5.29      3.22   29.6
## 14    18 0.826000 -1.4900          0.981    3.82      2.16   23.2
## 15    20 0.813000 -1.5100          0.961    2.81      1.51   18.3
## TOM calculation: adjacency..
## ..will not use multithreading.
##  Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
 softPower()  # soft power curve 

  modulePlot()  # plot modules  

  listWGCNA.Modules.out = listWGCNA.Modules() #modules
 input_selectGO5 <- 'GOBP'  #Gene set category 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO5,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_selectWGCNA.Module <- 'Entire network'   #Select a module
 input_topGenesNetwork <- 10    #SoftPower to cutoff
 input_edgeThreshold <- 0.4 #Number of top genes 
 moduleNetwork()    # show network of top genes in selected module
##  softConnectivity: FYI: connecitivty of genes with less than 15 valid samples will be returned as NA.
##  ..calculating connectivities..

 input_removeRedudantSets <- TRUE   #Remove redundant gene sets 
 results <- networkModuleGO()  #Enrichment analysis of selected module
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
adj.Pval Genes Pathways
5.3e-93 260 Response to abiotic stimulus
1.1e-51 173 Response to oxygen-containing compound
4.1e-49 146 Cellular response to stress
5.1e-47 161 Cellular response to chemical stimulus
5.3e-46 185 Response to organic substance
3.6e-45 63 Cellular response to hypoxia
4.1e-45 63 Cellular response to decreased oxygen levels
4.1e-45 63 Cellular response to oxygen levels
1.1e-41 63 Response to hypoxia
2.8e-41 63 Response to decreased oxygen levels